Experience in the Use of Automated Energy Storage Systems Based on Lithium-Ion Batteries in Autonomous Solar-Diesel Power Plants
Keywords:
electric energy storage systems, energy efficiency improvement, renewable energy, lithium-ion batteries
Abstract
The market of electric power storage systems is developing intensively: technologies are being improved, and experience of their practical application is being gained. These systems make it possible to solve many problems of controlling normal and emergency modes of power systems in a new way. The predominant share of electricity generation at small-scale power plants is produced by diesel, gas piston and gas turbine installations. At the same time, the requirements for energy storage devices in terms of power and energy capacity are currently quite moderate and feasible, which makes it possible to work out algorithms and control laws for them. With the improvement of technology and inevitable reduction in cost, storage systems will also be in demand in the "big" energy sector. The article describes the experience gained with development and use of automated energy storage systems based on lithium-ion batteries as part of autonomous hybrid solar-diesel power plants to improve the energy efficiency of the latter. It is shown that many important problems of choosing the composition of equipment, organization of structure, maintenance of modes, stability and reliability of power systems with the help of energy storage devices can be solved more efficiently than by using conventional methods. Options for solving such problems as reducing peak loads on generating equipment, power flow control, improving the quality of electricity (frequency, voltage), project economics (costs and profits), and energy storage (autonomy/redundancy) are considered.
References
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#
1. GOST 32144-2013. Elektricheskaya energiya. Sovmestimost' tekhnicheskikh sredstv elektromagnitnaya. Normy kachestva elektricheskoy energii v sistemakh elektrosnabzheniya obshchego naznacheniya (Electric Energy. Electromagnetic Compatibility of Technical Equipment. Power Quality Limits in the Public Power Supply Systems). M.: Standartinform, 2014, 16 p.
2. ABB Website. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Electron. resource], URL: http://www.abb.com (Date of appeal 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI:10.1016/ j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Sustainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019. 2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Kuvshinov V.V., Morozova N.V., Kuznetsov P.N. Ustanovki dlya solnechnoy energetiki (Installations for Solar Energy). М.: Sputnik +, 2017, 177 p.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
2. Сайт ABB. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Электрон. ресурс], URL: http://www.abb.com (дата обращения 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI: 10.1016/j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Su-stainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019.2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Кувшинов В.В., Морозова Н.В., Кузнецов П.Н. Установки для солнечной энергетики. М.: Спутник +, 2017, 177 с.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
#
1. GOST 32144-2013. Elektricheskaya energiya. Sovmestimost' tekhnicheskikh sredstv elektromagnitnaya. Normy kachestva elektricheskoy energii v sistemakh elektrosnabzheniya obshchego naznacheniya (Electric Energy. Electromagnetic Compatibility of Technical Equipment. Power Quality Limits in the Public Power Supply Systems). M.: Standartinform, 2014, 16 p.
2. ABB Website. Energy Storage Keeping Smart Grids in Balance, Zurich, Switzerland. [Electron. resource], URL: http://www.abb.com (Date of appeal 06.11.2021).
3. Liu C., et al. Virtual Power Plants for a Sustainable Urban Future. – Sustainable Cities and Society, 2020, 65(4), 102640, DOI:10.1016/ j.scs.2020.102640.
4. Fang F., et al. Uncertainties of Virtual Power Plant: Problems and Countermeasures. – Applied Energy, 2019, 239, pp. 454–470, DOI:10.1016/j.apenergy.2019.01.224.
5. Luo Z., Hong S., Ding Yu. A Data Mining-Driven Incentive-Based Demand Response Scheme for a Virtual Power Plant. Appl. Energy, 2019, 239, 549–559, DOI:10.1016/j.apenergy.2019.01.142.
6. Agamah S.U., Ekonomou L. Energy Storage System Scheduling for Peak Demand Reduction Using Evolutionary Combinatorial Optimisation. – Sustainable Energy Technologies and Assessment, 2017, 23, 73–82, DOI: 10.1016/J.SETA.2017.08.003.
7. Yang H., et al. Distributed Optimal Dispatch of Virtual Power Plant via Limited Communication. – IEEE Transactions on Power Systems, 2013, 28, 3511–3512, DOI:10.1109/TPWRS.2013.2242702.
8. Shah C., Wies R. Algorithms for Optimal Power Flow in Isolated Distribution Networks Using Different Battery Energy Storage Models. – IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2020, DOI: 10.1109/ISGT45199.2020.9087717.
9. Mohseni-Bonab S.M., et al. Voltage Security Constrained Stochastic Programming Model for Day-Ahead BESS Schedule in Co-Optimization of T&D Systems. – IEEE Transactions on Sustainable Energy, 2020,vol. 11, No. 1, pp. 391–404, DOI: 10.1109/TSTE.2019. 2892024.
10. Sun H., et al. Master–Slave-Splitting Based Distributed Global Power Flow Method for Integrated Transmission and Distribution Analysis. – IEEE Transactions on Smart Grid, 2015, vol. 6, No. 3, pp. 1484-1492, DOI: 10.1109/TSG.2014.2336810.
11. Behi B., et al. Cost–Benefit Analysis of a Virtual Power Plant Including Solar PV, Flow Battery, Heat Pump, and Demand Management: A Western Australian Case Study. – Energies 2020, 13(10), 2614, DOI:10.3390/en13102614.
12. Mansor M.A., et al. Construction and Performance Investigation of Three-Phase Solar PV and Battery Energy Storage System Integrated UPQC. IEEE Access 2020, 8, 103511–103538, DOI: DOI:10.1109/ACCESS.2020.2997056.
13. Joshua A.M., Vittal K.P. Transient Behavioural Modelling of Battery Energy Storage System Supporting Microgrid. – IEEE Int. Conf. on Power Electronics, Smart Grid and Renewable Energy, 2020, DOI: 10.1109/PESGRE45664.2020.9070389.
14. Hasan M.M., et al. A Multi-Objective Co-Design Optimization Framework for Grid-Connected Hybrid Battery Energy Storage Systems: Optimal Sizing and Selection of Technology. – Energies, 2022, 15 (15), 5355, DOI: 10.3390/en15155355.
15. Kuvshinov V.V., Morozova N.V., Kuznetsov P.N. Ustanovki dlya solnechnoy energetiki (Installations for Solar Energy). М.: Sputnik +, 2017, 177 p.
16. Kumar R., Channi H.K. A PV-Biomass off-grid Hybrid Renewable Energy System (HRES) for Rural Electrification: Design, Optimization and Techno-Economic-Environmental Analysis. – Journal of Cleaner Production, 2022, vol. 349, 131347, DOI: 10.1016/j.jclepro.2022.131347
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2021-11-06
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